Physical samples are foundational entities for research across biological, Earth, and environmental sciences. Data generated from sample-based analyses are not only the basis of individual studies, but can also be integrated with other data to answer new and broader-scale questions. Ecosystem studies increasingly rely on multidisciplinary team-science to study climate and environmental changes. While there are widely adopted conventions within certain domains to describe sample data, these have gaps when applied in a multidisciplinary context. In this study, we reviewed existing practices for identifying, characterizing, and linking related environmental samples. We then tested practicalities of assigning persistent identifiers to samples, with standardized metadata, in a pilot field test involving eight United States Department of Energy projects. Participants collected a variety of sample types, with analyses conducted across multiple facilities. We address terminology gaps for multidisciplinary research and make recommendations for assigning identifiers and metadata that supports sample tracking, integration, and reuse. Our goal is to provide a practical approach to sample management, geared towards ecosystem scientists who contribute and reuse sample data.

The paper aims to present the implementation of the RDA research data policy framework in Slovenian scientific journals within the project RDA Node Slovenia. The activity aimed to implement the practice of data sharing and data citation in Slovenian scientific journals and was based on internationally renowned practices and policies, particularly the Research Data Policy Framework of the RDA Data Policy Standardization and Implementation Interest Group. Following this, the RDA Node Slovenia coordination prepared a guidance document that allowed the four pilot participating journals (from fields of archaeology, history, linguistics and social sciences) to adjust their journal policies regarding data sharing, data citation, adapted the definitions of research data and suggested appropriate data repositories that suit their disciplinary specifics. The comparison of results underlines how discipline-specific the aspects of data-sharing are. The pilot proved that a grass-root approach in advancing open science can be successful and well-received in the research community, however, it also pointed out several issues in scientific publishing that would benefit from a planned action on a national level. The context of an underdeveloped data sharing culture, slow implementation of open data strategy by the national research funder and sparse national data service infrastructure creates a unique environment for this study, the result of which can be used in similar contexts worldwide.

The SHARC Interest Group of the Research Data Alliance was established to improve research crediting and rewarding mechanisms for scientists who wish to organise their data (and material resources) for community sharing. This requires that data are findable and accessible on the Web, and comply with shared standards making them interoperable and reusable in alignment with the FAIR principles. It takes considerable time, energy, expertise and motivation. It is imperative to facilitate the processes to encourage scientists to share their data. To that aim, supporting FAIR principles compliance processes and increasing the human understanding of FAIRness criteria – i.e., promoting FAIRness literacy – and not only the machine-readability of the criteria, are critical steps in the data sharing process. Appropriate human-understandable criteria must be the first identified in the FAIRness assessment processes and roadmap. This paper reports on the lessons learned from the RDA SHARC Interest Group on identifying the processes required to prepare FAIR implementation in various communities not specifically data skilled, and on the procedures and training that must be deployed and adapted to each practice and level of understanding. These are essential milestones in developing adapted support and credit back mechanisms not yet in place.

Repositories increasingly accept research outputs and associated artifacts that underlie reported findings, leading to potential changes in the demand for data curation and repository services. This paper describes a curation tool that responds to this challenge by economizing and optimizing curation efforts. The curation tool is implemented at Yale University’s Institution for Social and Policy Studies (ISPS) as YARD. By standardizing the curation workflow, YARD helps create high quality data packages that are findable, accessible, interoperable, and reusable (FAIR) and promotes research transparency by connecting the activities of researchers, curators, and publishers through a single pipeline.

Muon hodoscope URAGAN continuously detects the angular distribution of muons in a wide range of zenith angles and allows one to obtain information about variations, both in the intensity and in angular characteristics of the muon flux, caused by active processes in the heliosphere, the magnetosphere and atmosphere of the Earth. Various parameters of the muon flux anisotropy and methods of calculation of these parameters are discussed. Real-time processing of a continuous flow of multidimensional data from the muon hodoscope URAGAN is quite a challenge. In the article, methods of formation and primary analysis of the data, their processing in real time and obtaining time series of various parameters of integral counting rate and angular anisotropy of the muon flux, which are important for the physical analysis of modulation processes of cosmic rays in the heliosphere, are presented.

Providing smallholder farmers with support through conventional government extension approaches is challenging as the number of extension agents is decreasing. At the same time, new information and communication technologies (ICTs), such as short message services (SMS) sent via mobile phones, show considerable promise to complement existing extension services. In the UP-scaling Technology in Agriculture through Knowledge and Extension (UPTAKE) Project, ICTs were used to create awareness and increase uptake and adoption of agricultural innovations by maize farmers in Tanzania. Two SMS-based maize campaigns were implemented during the 2016/2017 and 2017/2018 cropping seasons in the Southern Highlands of Tanzania. Prior to the start of the campaigns, formative research to determine maize production knowledge, practices and challenges was conducted in Mbeya and Songwe Region. After the campaign a telephone survey, key informant interviews and focus group discussions were conducted. During the campaign, about 3.8 million SMS were disseminated to over 55,000 farmers. 73% were male, 19% owned smart phones and 86% farmed maize on up to 1.2 hectares of land. Farmers reported maize production challenges as: unreliable markets, inadequate extension services, pest outbreaks and lack of knowledge to identify counterfeit inputs particularly seeds and fertilizers. The UPTAKE mobile SMS campaign was a new approach to agricultural extension in this area. A telephone survey amongst a sample of farmers who received the SMS revealed that 53% of respondents considered that this was now their preferred as a source of information compared to traditional sources including neighbours and family members, demonstration plots, agricultural extension workers and radios. Key lessons learnt relate to management of databases of farmer contacts, importance of participatory processes in developing content and designing SMS campaigns, and the need for flexibility and promptness in responding to emerging threats such as delayed rains and outbreaks of pests. Good practices like buy in and authorizations from the government administrative structures and compliance with country’s regulations on communication are integral to the success of ICT projects.

The purpose of this paper is to explore current data management training needs and practices for Belmont Forum member agencies and researchers to inform a Toolkit. Fourteen Belmont Forum affiliated individuals were interviewed following a predetermined set of questions to create data curation profiles of their funded work. The data curation profile questionnaire includes questions related to data management, storage, stakeholders, costs, training, and credentials. The interview findings highlight gaps in existing knowledge of data management theory and practice that could impact data re-use. Results of these interviews were used to populate a Toolkit of data management training and effective practice resources specifically developed to train Belmont Forum grant awardees. The results also highlight some attitudes and behaviours of current scientists, researchers, and agency representatives, and the impact of the implementation of data management plans on the open science movement.

In this paper, we explain our strategy for developing research data management policies at TU Delft. Policies can be important drivers for research institutions in the implementation of good data management practices. As Rans and Jones note (Rans and Jones 2013), “Policies provide clarity of purpose and may help in the framing of roles, responsibilities and requisite actions. They also legitimise making the case for investment”. However, policy development often tends to place the researchers in a passive position, while they are the ones managing research data on a daily basis. Therefore, at TU Delft, we have taken an alternative approach: a policy needs to go hand in hand with practice. The policy development was initiated by the Research Data Services at TU Delft Library, but as the process continued, other stakeholders, such as legal and IT departments, got involved. Finally, the faculty-based Data Stewards have played a key role in leading the consultations with the research community that led to the development of the faculty-specific policies. This allows for disciplinary differences to be reflected in the policies and to create a closer connection between policies and day-to-day research practice. Our primary intention was to keep researchers and research practices at the centre of our strategy for data management. We did not want to introduce and mandate requirements before adequate infrastructure and professional support were available to our research community and before our researchers were themselves willing to discuss formalisation of data management practices. This paper describes the key steps taken and the most important decisions made during the development of RDM policies at TU Delft.

In the case of the African continent, the estimates of most climate forcing components are associated with large uncertainties, above all the greenhouse gas budget. The EU-funded SEACRIFOG project is designing an observation network which aims at reducing these uncertainties. In this practice paper, we present the various steps towards the design of this network and discuss the data-related implications. This includes the formulation of appropriate observational requirements for each variable considered essential to quantify Africa-wide climate forcing as well as an assessment of corresponding available observational infrastructures and data in order to determine data gaps, needs and priorities. The results are intended to inform the design of an interoperable African data infrastructure for environmental observations.

This work is devoted to the study of applicability of modern methods of machine learning to the task of automatic classification of scientific articles and abstracts. For this purpose, the study of such models of machine learning as artificial neural networks, random forest, logistic regression, and support vector machine was carried out with taking into account such a feature of scientific texts as a large number of terms specific for various categories. Separately, the stages of data collection and extraction of text characteristics are considered. The results of research are used in development of a decision support system for assignment of scientific texts to the code of the department or abstract journal of All-Russian Institute of Scientific and Technical Information of Russian Academy of Sciences.